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Abstract:

A method for visualizing seismic data of a subterranean formation,
including obtaining an estimated dip field of the subterranean formation,
wherein the estimated dip field represents a measure of deviation of a
stratigraphic layer from flat, extracting a matrix data item surrounding
a voxel of the seismic data, wherein the matrix data item is extracted
from the seismic data based on a value of the estimated dip field
surrounding the voxel, generating modified seismic data by at least
applying a matrix operator to the seismic data, wherein the matrix
operator calculates a partial derivative of the seismic data using the
matrix data item, and displaying the modified seismic data.

Claims:

1. A method for visualizing seismic data of a subterranean formation,
comprising: obtaining an estimated dip field of the subterranean
formation, wherein the estimated dip field represents a measure of
deviation of a stratigraphic layer from flat; extracting, by a computer
processor, a matrix data item surrounding a voxel of the seismic data,
wherein the matrix data item is extracted from the seismic data based on
a value of the estimated dip field surrounding the voxel; generating, by
the computer processor, modified seismic data, the generating comprising:
applying a matrix operator to the seismic data, wherein the matrix
operator calculates a partial derivative of the seismic data using the
matrix data item; and displaying the modified seismic data.

2. The method of claim 1, wherein the matrix operator comprises a three
dimensional (3D) operator, wherein the matrix data item comprises a 3 by
3 by 3 matrix represented by Data(x, y, z) for x, y, z in {-1, 0, 1},
wherein the voxel comprises the seismic data, represented by Input(i, j,
k), at position (i, j, k) of the voxel space, wherein the matrix data
item is extracted based on Data(x, y, z)=Input(i+x, j+y,
k+z+x*dipIL(i, j, k)+y*dipXL(i, j, k)), wherein dipIL(i,
j, k) represents an inline dip of the estimated dip field at the position
(i, j, k) of the voxel space, and wherein dipXL(i, j, k) represents
a crossline dip of the estimated dip field at the position (i, j, k) of
the voxel space.

4. The method of claim 1, wherein generating the modified seismic data
further comprises: adjusting, based on pre-determined weighting factors,
contributions of first, second, and third partial derivatives to the
modified seismic data along a perpendicular direction of the
stratigraphic layer, wherein the matrix operator comprises first, second,
and third matrix operators for calculating the first, second, and third
partial derivatives, respectively, along at least three orthogonal
directions.

5. The method of claim 1, wherein the modified seismic data is
represented by S= {square root over
(Gx2+Gy2+Wz2Gz2)} wherein
Gx, Gy, and Gz are proportional to the first, second, and
third partial derivatives, respectively, and wherein Wz is a
pre-determined fraction.

6. The method of claim 5, wherein Wz is in the range of
approximately [0, 0.4].

7. The method of claim 1, wherein generating the modified seismic data
further comprises: normalizing contribution of the partial derivative to
the modified seismic data based on a magnitude of the seismic data.

8. A system for visualizing seismic data of a subterranean formation,
comprising: a seismic data processing module executing on a computer
processor and configured to: obtain an estimated dip field of the
subterranean formation, wherein the estimated dip field represents a
measure of deviation of a stratigraphic layer from flat; extract a matrix
data item surrounding a voxel of the seismic data, wherein the matrix
data item is extracted from the seismic data based on a value of the
estimated dip field surrounding the voxel; and generate modified seismic
data, wherein generating the modified seismic data comprises: apply a
matrix operator to the seismic data, wherein the matrix operator
calculates a partial derivative of the seismic data using the matrix data
item; and a display device configured to display the modified seismic
data.

9. The system of claim 8, wherein the matrix operator comprises a three
dimensional (3D) operator, wherein the matrix data item comprises a 3 by
3 by 3 matrix represented by Data(x, y, z) for x, y, z in {-1, 0, 1},
wherein the voxel comprises the seismic data, represented by Input(i, j,
k), at position (i, j, k) of the voxel space, wherein the matrix data
item is extracted based on Data(x, y, z)=Input(i+x, j+y,
k+z+x*dipIL(i, j, k)+y*dipXL(i, j, k)), wherein dipIL(i,
j, k) represents an inline dip of the estimated dip field at the position
(i, j, k) of the voxel space, and wherein dipXL(i, j, k) represents
a crossline dip of the estimated dip field at the position (i, j, k) of
the voxel space.

10. The system of claim 8, wherein the matrix operator comprises a Sobel
operator.

11. The system of claim 8, wherein generating the modified seismic data
further comprises: adjusting, based on pre-determined weighting factors,
contributions of first, second, and third partial derivatives to the
modified seismic data along a perpendicular direction of the
stratigraphic layer, wherein the matrix operator comprises first, second,
and third matrix operators calculate the first, second, and third partial
derivatives, respectively, along three orthogonal directions.

12. The system of claim 8, wherein the modified seismic data is
represented by S= {square root over
(Gx2+Gy2+Wz2Gz2)}, wherein
GT, Gy, and Gz are proportional to the first, second, and
third partial derivatives, respectively, and wherein Wz is a
pre-determined fraction.

13. The system of claim 12, wherein Wz is in the range of
approximately [0, 0.4].

14. The system of claim 8, wherein generating the modified seismic data
further comprises: normalizing contribution of the partial derivative to
the modified seismic data based on a magnitude of the seismic data.

15. A non-transitory computer readable medium storing instructions for
visualizing seismic data of a subterranean formation, the instructions
when executed causing a processor to: obtain an estimated dip field of
the subterranean formation, wherein the estimated dip field represents a
measure of deviation of a stratigraphic layer from flat; extract a matrix
data item surrounding a voxel of the seismic data, wherein the matrix
data item is extracted from the seismic data based on a value of the
estimated dip field surrounding the voxel; generate modified seismic
data, wherein generating the modified seismic data comprises: applying a
matrix operator to the seismic data, wherein the matrix operator
calculates a partial derivative of the seismic data using the matrix data
item; and displaying the modified seismic data.

16. The non-transitory computer readable medium of claim 15, wherein the
matrix operator comprises a three dimensional (3D) operator, wherein the
matrix data item comprises a 3 by 3 by 3 matrix represented by Data(x, y,
z) for x, y, z in {-1, 0, 1}, wherein the voxel comprises the seismic
data, represented by Input(i, j, k), at position (i, j, k) of the voxel
space, wherein the matrix data item is extracted based on Data(x, y,
z)=Input(i+x, j+y, k+z+x*dipIL(i, j, k)+y*dipXL(i, j, k)),
wherein dipIL(i, j, k) represents an inline dip of the estimated dip
field at the position (i, j, k) of the voxel space, and wherein
dipXL(i, j, k) represents a crossline dip of the estimated dip field
at the position (i, j, k) of the voxel space.

18. The non-transitory computer readable medium of claim 15, wherein
generating the modified seismic data further comprises: adjusting, based
on pre-determined weighting factors, contributions of first, second, and
third partial derivatives to the modified seismic data along a
perpendicular direction of the stratigraphic layer, wherein the matrix
operator comprises first, second, and third matrix operators calculate
the first, second, and third partial derivatives, respectively, along
three orthogonal directions.

19. The non-transitory computer readable medium of claim 15, wherein the
modified seismic data is represented by S= {square root over
(Gx2+Gy2+Wz2Gz2)} wherein
Gx, Gy, and Gz are proportional to the first, second, and
third partial derivatives, respectively, and wherein Wz is a
pre-determined fraction.

20. The non-transitory computer readable medium of claim 19, wherein
Wz is in the range of approximately [0, 0.4].

21. The non-transitory computer readable medium of claim 15, wherein
generating the modified seismic data further comprises: normalizing
contribution of the partial derivative to the modified seismic data based
on a magnitude of the seismic data.

[0002] Approaches exist in the industry for fault and salt body detection
based on the premise that seismic faulting and salt introduce
discontinuities in the seismic horizons. Several seismic attributes
(e.g., chaos, coherence, variance, curvature, and Sobel filter
attributes, etc.) have been used to enhance this discontinuity.
Subsequent to the enhancement, the structures are extracted and compared
to the original seismic data for quality control.

[0003] The Sobel operator is used in image processing, particularly within
edge detection algorithms. Technically, it is a discrete differentiation
operator, computing an approximation of the opposite of the gradient of
the image intensity function. At each point in the image, the result of
the Sobel operator is either the corresponding opposite of the gradient
vector or the norm of this vector. The Sobel operator is based on
convolving the image with a small, separable, and integer valued filter
in horizontal and vertical directions and is therefore relatively
inexpensive in terms of computations. On the other hand, the opposite of
the gradient approximation that it produces is relatively crude, in
particular for high frequency variations in the image. Mathematically,
the Sobel operator uses two 3×3 kernels which are convolved with
the original image to calculate approximations of the derivatives--one
for horizontal changes, and one for vertical. If A represents the source
image, and Gx and Gy represent two images which at each point
contain the horizontal and vertical derivative approximations, the two
dimensional Sobel operators are shown in a 3 by 3 matrix form as follows:

[0004] In general, in one aspect, the invention relates to a method for
visualizing seismic data of a subterranean formation. The method may
include obtaining an estimated dip field of the subterranean formation,
wherein the estimated dip field represents a measure of deviation of a
stratigraphic layer from flat, extracting, by a computer processor, a
matrix data item surrounding a voxel of the seismic data, wherein the
matrix data item is extracted from the seismic data based on a value of
the estimated dip field surrounding the voxel, generating, by the
computer processor, modified seismic data. The generating may include
applying a matrix operator to the seismic data, wherein the matrix
operator may calculate a partial derivative of the seismic data using the
matrix data item, and displaying the modified seismic data.

[0005] Other aspects of the invention will be apparent from the following
detailed description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

[0006] The appended drawings illustrate several embodiments of amplitude
contrast seismic attribute and are not to be considered limiting of its
scope, for amplitude contrast seismic attribute may admit to other
equally effective embodiments.

[0007]FIG. 1 depicts a survey operation to measure properties of the
subterranean formation in which one or more embodiments of amplitude
contrast seismic attribute may be implemented.

[0008] FIG. 2 shows a system for processing seismic data based on
amplitude contrast seismic attribute in accordance with one or more
embodiments.

[0009]FIG. 3 depicts an example method of processing seismic data based
on amplitude contrast seismic attribute in accordance with one or more
embodiments.

[0010] FIGS. 4.1-4.5 depict an example of processing seismic data based on
amplitude contrast seismic attribute in accordance with one or more
embodiments.

[0011]FIG. 5 depicts a computer system using which one or more
embodiments of processing seismic data based on amplitude contrast
seismic attribute may be implemented.

DETAILED DESCRIPTION

[0012] Aspects of the present disclosure are shown in the above-identified
drawings and described below. In the description, like or identical
reference numerals are used to identify common or similar elements. The
drawings are not necessarily to scale and certain features may be shown
exaggerated in scale or in schematic in the interest of clarity and
conciseness.

[0013] Aspects of the present disclosure include a method, system, and
computer readable medium of processing and visualizing seismic data using
amplitude contrast. Amplitude contrast is a Sobel based attribute that
has been used in detecting structural geology. Similar to the Sobel
filter, amplitude contrast is a computation of the amplitude derivatives
between neighboring traces where the non-diagonal neighbors are weighted
twice as much. The calculated differences are then normalized and the
final value is calculated using Equations (1), (2), (3) and (4) (below),
where Sx, Sy, and Sz are the weighting operators in the
corresponding dimensions. Here the values are squared to avoid negative
differences, and finally the square root of the sum of the squared values
is calculated as the result.

[0014]FIG. 1 depicts a survey operation being performed by a seismic
recording truck (106a) to measure properties of the subterranean
formation. The survey operation is a seismic survey operation for
producing sound vibrations. In FIG. 1, an acoustic source (110) produces
sound vibrations (112) that reflect off a number of horizons (114) and
other structures such as a fault (116) in an earth formation. The sound
vibration(s) (112) is (are) received by sensors, such as
geophone-receivers (118), situated on the earth's surface, and the
geophones (118) produce electrical output signals, referred to as data
received (120) in FIG. 1.

[0015] The received sound vibration(s) (112) are representative of
different parameters, such as amplitude and/or frequency. The data
received (120) is provided as input data to a computer (122a) of the
seismic recording truck (106a), and responsive to the input data, the
recording truck computer (122a) generates a seismic data output record
(124). The seismic data output record (124) may be further processed and
presented, by the seismic data visualization system (200), to a user
(e.g., a geologist, an oilfield engineer, etc.) for performing field
operations, such as extracting subterranean assets, etc. For example, the
seismic data visualization system (200) may be located in a surface unit
of the field for extracting the subterranean assets, or located in a
remote computing facility away from the field. The subterranean assets
include but are not limited to hydrocarbons such as oil. Throughout this
document, the terms "oilfield" and "oilfield operation" may be used
interchangeably with the terms "field" and "field operation" to refer to
a site where any types of valuable fluids can be found and the activities
required for extracting them. The terms may also refer to sites where
substances are deposited or stored by injecting them into the surface
using boreholes and the operations associated with this process. Further,
the term "field operation" refers to a field operation associated with a
field, including activities related to field planning, wellbore drilling,
wellbore completion, and/or production using the wellbore.

[0016] In one or more embodiments of the invention, seismic data output
record (124) is processed by the seismic data visualization system (200).
Throughout this document, the seismic data output record (124) is
generally referred to as seismic, seismic data, seismic cube, or seismic
volume. Generally, the seismic volume consists of a large number of basic
seismic data elements referred to as voxels and is also referred to as a
voxel space. The 3D edge detection operator is applied at every voxel in
the seismic cube. Edges in seismic can be seen as changes in seismic
amplitude caused by discontinuities such as faults and fractures. Since
seismic layers also are seen as changes in amplitudes, a direct 3D Sobel
operator will highlight both stratigraphic layers and discontinuities in
the seismic data. These highlighted stratigraphic layers obscure the
visualization of the discontinuities, such as fault or salt body. In one
or more embodiments, three modifications are applied that are different
from a basic 3D Sobel operator in order to focus on the edges in the
seismic data representing discontinuities. The three modifications are
structure oriented data extraction, introducing axis weights, and
amplitude normalization. The first two modifications cause the filter to
focus edge detection on discontinuities and chaotic seismic, e.g. the
lack of well defined stratigraphic structure. The third modification, the
normalization, causes the amplitude changes to be measured relative to
the input amplitude, e.g. it causes chaotic low amplitude regions also to
be highlighted.

[0017] FIG. 2 shows details of the seismic data visualization system (200)
in which one or more embodiments of amplitude contrast seismic attribute
may be implemented. As shown in FIG. 2, the seismic data visualization
system (200) includes a seismic data processing module (201), a display
device (202), and a repository (210). In one or more embodiments, one or
more of the modules and elements shown in FIG. 2 may be omitted,
repeated, and/or substituted. Accordingly, embodiments of amplitude
contrast seismic attribute should not be considered limited to the
specific arrangements of modules shown in FIG. 2.

[0018] As noted above, seismic data output record (124) of the
subterranean formation is sent and stored in the repository (210) of the
seismic data processing module (201) as seismic data (211) for
processing. In one or more embodiments, the seismic data processing
module (201) may perform the following: (i) obtain an estimated dip field
of the subterranean formation, where the estimated dip field represents a
measure of deviation of a stratigraphic layer from flat; (ii) extract a
matrix data item surrounding a voxel of the seismic data (211), where the
matrix data item is extracted from the seismic data (211) based on a
value of the estimated dip field surrounding the voxel; and (iii)
generate modified seismic data (212) by at least applying a matrix
operator to the matrix data item, where the matrix operator calculates a
partial derivative of the seismic data (211) using the matrix data item.
In one or more embodiments, the modified seismic data (212) is displayed
by the display device (202) (e.g., a two-dimensional display, a
three-dimensional display, or any suitable computer display device) for
visualization by the user. Additional details of generating the modified
seismic data (212) from the seismic data (211) are described below.

[0019] Examples of non-flat stratigraphic layers are represented in the
example seismic section shown in FIG. 4.1, where the deviation from flat
varies throughout the subterranean formation. In one or more embodiments,
the aforementioned matrix data item is a 3 by 3 by 3 matrix extracted
from the seismic data (211) surrounding a voxel of the seismic data (211)
and is mathematically represented by Data(x, y, z) for x, y, z in {-1, 0,
1}, i.e., x, y, or z can be -1, 0, or 1 to form the 3 by 3 by 3 matrix.
The term voxel refers to an element in the seismic data (211),
represented by Input (i, j, k), at position (i, j, k) of the voxel space.
Accordingly, the matrix data item is extracted based on Data(x, y,
z)=Input(i+x, j+y, k+z+x*dipIL(i, j, k)*dipXL(i, j, k)), where
dipIL(i, j, k) represents an inline dip of the estimated dip field
at the position (i, j, k) of the voxel space, and dipXL(i, j, k)
represents a crossline dip of the estimated dip field at the position (i,
j, k) of the voxel space.

[0020] In one or more embodiments, the modified seismic data is generated
by applying first, second, and third matrix operators to the seismic
data. In particular, these matrix operators may calculate first, second,
and third partial derivatives, respectively, of the seismic data along
three orthogonal directions using the matrix data item. For example the
first, second, and third matrix operators may be a 3D Sobel operator or a
variation thereof. In one or more embodiments, contributions of the
first, second, and third partial derivatives to the modified seismic data
is adjusted, based on pre-determined weighting factors, along a
perpendicular direction of the stratigraphic layer. For example, the
modified seismic data may be mathematically represented by

S= {square root over
(Gx2+Gy2+Wz2Gz2)},

[0021] wherein Gx, Gy, and Gz are proportional to the
first, second, and third partial derivatives, respectively, and where
Wz is a pre-determined fraction. In one or more embodiments, Wz
is in the range of approximately [0, 0.4]. In one or more embodiments,
contributions of the partial derivatives to the modified seismic data are
further normalized based on a magnitude of the seismic data. Additional
details of generating the modified seismic data for visualization are
described in reference to FIGS. 4.1-4.3 below.

[0022]FIG. 3 depicts a flow chart of an example method for processing
seismic data for amplitude contrast seismic attribute in accordance with
one or more embodiments. For example, the method depicted in FIG. 3 may
be practiced using the seismic data visualization system (200) described
in reference to FIGS. 1 and 2 above. In one or more embodiments, one or
more of the elements shown in FIG. 3 may be omitted, repeated, and/or
performed in a different order.

[0023] Generally, the method depicted in FIG. 3 allows a user to view
(i.e., visualize) seismic data of a subterranean formation with enhanced
clarity. In Block 301, an estimated dip field of the subterranean
formation is obtained to represent a measure of deviation of a
stratigraphic layer from flat. Further, a matrix data item (e.g., a 3 by
3 by 3 matrix) is extracted from the seismic data surrounding a voxel of
the seismic data. In one or more embodiments, the matrix data item is
extracted from the seismic data based on a value of the estimated dip
field surrounding the voxel. This is referred to as the structure
oriented data extraction. Mathematically, the 3 by 3 by 3 matrix of the
matrix data item is represented by Data(x, y, z) for x, y, z in {-1, 0,
1}, i.e., x, y, or z can be -1, 0, or 1 to form the 3 by 3 matrix. The
voxel of the seismic data is represented by Input (i, j, k), at position
(i, j, k) of the voxel space. The matrix data item is extracted based on
Data(x, y, z)=Input(i+x, j+y, k+z+x*dipIL(i, j, k)+y*dipXL(i,
j, k)), where dipIL(i, j, k) represents an inline dip of the
estimated dip field at the position (i, j, k) of the voxel space, and
dipXL(i, j, k) represents a crossline dip of the estimated dip field
at the position (i, j, k) of the voxel space.

[0024] In Block 302, seismic data is modified to generate modified seismic
data by at least applying a matrix operator to the seismic data. In
particular, the matrix operator calculates one or more partial
derivatives of the seismic data using the matrix data item. In one or
more embodiments, the matrix operator is a three dimensional (3D)
operator, such as a Sobel operator, that generates first, second, and
third partial derivatives of the seismic data along a perpendicular
direction of the stratigraphic layer using the matrix data item.

[0025] In Block 303, contributions of the first, second, and third partial
derivatives to the modified seismic data are adjusted, based on
pre-determined weighting factors. This is referred to as introducing axis
weights into the seismic data. In one or more embodiments, the modified
seismic data is mathematically represented by

S= {square root over
(Gx2+Gy2+Wz2Gz2)}

[0026] wherein Gx, Gy, and Gz are proportional to the
first, second, and third partial derivatives, respectively, and where
Wz is a pre-determined fraction. In one or more embodiments, Wz
is in the range of approximately [0, 0.4].

[0027] In Block 304, contributions of the partial derivatives to the
modified seismic data are further normalized based on a magnitude of the
seismic data. This is referred to as amplitude normalization.

[0028] In Block 305, the modified seismic data is displayed to a user.
Additional details of generating the modified seismic data for
visualization are described in reference to FIGS. 4.1-4.3 below.

[0029] FIGS. 4.1-4.5 depict an example of processing seismic data based on
the amplitude contrast seismic attribute in accordance with one or more
embodiments.

[0030]FIG. 4.1 shows a screenshot 1A (401a) of input seismic section
(corresponding to seismic data output record (124) of FIG. 1) where
stratigraphic layers (corresponding to horizons (114) of FIG. 1) are
clearly delineated, and to certain extent obscuring the fault
(corresponding to fault (116) of FIG. 1) intersecting the stratigraphic
layers. In addition, FIG. 4.1 shows a screenshot 1B (401b) of amplitude
contrast output, which is the modified seismic data based on the three
aforementioned modifications of Sobel operator, namely: structure
oriented data extraction, introducing axis weights, and amplitude
normalization. As noted above, the first two modifications cause the
filter to focus edge detection on discontinuities and chaotic seismic,
and, to certain extent, suppresses the well defined stratigraphic
structure to bring out the image of the fault with more clarity. The
third modification, the normalization, causes the amplitude changes to be
measured relative to the input amplitude, e.g. it causes chaotic low
amplitude regions (e.g., a salt body) also to be highlighted.

[0031]FIG. 4.2 shows a comparison between a screenshot 2A (402a) of input
seismic section processed based on amplitude contrast with structure
oriented data extraction and a screenshot 2B (402b) of input seismic
section processed based on plain amplitude contrast without dependence on
the dip field. Specifically, the input to the edge detection operator
used to generate the screenshot 2A (402a) is extracted according to an
estimated dip field in the following way. The dip specifies how much the
stratigraphic layers in the seismic deviate from flat. If all layers were
flat, a horizontal 2D operator would be well suited to detect
discontinuities. For each voxel in the seismic cube, the data is
extracted according to the mathematical formula described in FIG. 3 that
makes the input to the 3D operator seem to come from a seismic cube with
only flat layers.

[0032] In contrast to the formula Data(x, y, z)=Input(i+x, j+y,
k+z+x*dipIL(i, j, k)+y*dipXL(i, j, k)) described in FIG. 3, a
plain data extraction would be done using Data(x, y, z)=Input(i+x, j+y,
k+z). The effects of using these two different data extraction methods
are seen in FIG. 4.2. A grey shade is present in the steepest dipping
area when using plain data extraction as shown in the screenshot 2B
(402b). This shade is suppressed/eliminated using structure oriented
extraction as shown in the screenshot 2A (402a).

[0033] In order to extract values that are not exactly at an integer
coordinate in the input data, values may be interpolated using a higher
order polynomial, such as spline or interpolation function. For the
example shown in FIG. 4.2, cubic spline interpolation is used in the Z
direction.

[0034]FIG. 4.3 shows the effect of the 3D operator with axis weighting.
In essence the operator calculates the partial first derivatives in
dimensions X, Y, and Z for the 3 by 3 by 3 matrix data item. For example,
there are nine first derivative estimates for each principal axis. These
nine estimates are weighted so that more central estimates have higher
weight than the more distant estimates. As an example, the estimates in
the Z direction Dz are weighted and summed in the following manner:

[0035] For x and y in {-1, 0, 1}: G=sum (Dz(x,
y)×21-|x|)2.sup.(1-|y|)), Gx and Gy are calculated
in the same manner. An un-modified Sobel operation is calculated as:

S= {square root over (Gx2+Gy2+Gz2)}

[0036] However, this operator would detect all the stratigraphic layers in
the seismic, as shown in the screenshot 3D (403d), which is not what is
of interest here. Therefore, a modified operator is defined where the
contribution from Gz is weighted down relative to Gx and G.
This weight Wz is in the range of [0, 0.4]. The modified operator
then is defined as:

S= {square root over
(Gz2+Gy2+Wz2Gz2)}

[0037] The effect of varying Wz is seen in FIG. 4.3, where screenshot
3A (403a), screenshot 3B (403b), and screenshot 3C (403c) are based on Z
weight (i.e., Wz) of 0, 0.2, and 0.4, respectively. It is observed
that the faults (seen as traces in upside down "V" shapes intersecting
the stratigraphic layers) stand out more clearly when Wz approaches
0. For applications such as detecting salt, it has been observed that a
Wz of approximately 0.2 gives improved imaging of the salt
structures.

[0038]FIG. 4.4 shows the effect of the 3D operator with amplitude
normalization. Normalization of the output has the property that edges in
areas with high and low seismic amplitude areas become directly
comparable, as both will be scaled to the interval [0, 1]. This will give
better continuity for faults running through high and low amplitude
layers, and will also highlight chaotic low amplitude areas, such as salt
structures. Normalization is obtained by dividing the modified Sobel
operator result with the sum of the weighted absolute values used in the
computations of the partial derivatives, using the same weights as when
weighting the partial derivatives, and where Wz is also applied as
an extra weight for the values in the Z direction.

[0039] An example image of the effects of the normalization is shown as
the screenshot 4A (404a). In comparison, the same image without the
normalization is shown as the screenshot 4B (404b). For detection of
faults, the results are sometimes better without normalization. The
normalization step can be selectively turned on or off to achieve better
visualization clarity dependent on the seismic data. For detection of
salt structures, the normalization generally improves clarity as shown in
the three screenshots of FIG. 4.5. The input amplitude range shown in
FIG. 4.4 is fairly evenly distributed through out the input section. Thus
the faults are clear even on the non-normalized image shown in screenshot
4B (404b).

[0040] Embodiments of amplitude contrast seismic attribute may be
implemented on virtually any type of computer regardless of the platform
being used. For instance, as shown in FIG. 5, a computer system (500)
includes one or more processor(s) (502) such as a central processing unit
(CPU) or other hardware processor, associated memory (505) (e.g., random
access memory (RAM), cache memory, flash memory, etc.), a storage device
(506) (e.g., a hard disk, an optical drive such as a compact disk drive
or digital video disk (DVD) drive, a flash memory stick, etc.), and
numerous other elements and functionalities typical of today's computers
(not shown). The computer (500) may also include input means, such as a
keyboard (508), a mouse (510), or a microphone (not shown). Further, the
computer (500) may include output means, such as a monitor (512) (e.g., a
liquid crystal display LCD, a plasma display, or cathode ray tube (CRT)
monitor). The computer system (500) may be connected to a network (515)
(e.g., a local area network (LAN), a wide area network (WAN) such as the
Internet, or any other similar type of network) via a network interface
connection (not shown). Those skilled in the art will appreciate that
many different types of computer systems exist (e.g., workstation,
desktop computer, a laptop computer, a personal media device, a mobile
device, such as a cell phone or personal digital assistant, or any other
computing system capable of executing computer readable instructions),
and the aforementioned input and output means may take other forms, now
known or later developed. Generally speaking, the computer system (500)
includes at least the minimal processing, input, and/or output means
necessary to practice one or more embodiments.

[0041] Further, those skilled in the art will appreciate that one or more
elements of the aforementioned computer system (500) may be located at a
remote location and connected to the other elements over a network.
Further, one or more embodiments may be implemented on a distributed
system having a plurality of nodes, where each portion of the
implementation may be located on a different node within the distributed
system. In one or more embodiments, the node corresponds to a computer
system. Alternatively, the node may correspond to a processor with
associated physical memory. The node may alternatively correspond to a
processor with shared memory and/or resources. Further, software
instructions to perform one or more embodiments may be stored on a
computer readable medium such as a compact disc (CD), a diskette, a tape,
or any other computer readable storage device.

[0042] While amplitude contrast seismic attribute has been described with
respect to a limited number of embodiments, those skilled in the art,
having benefit of this disclosure, will appreciate that other embodiments
may be devised which do not depart from the scope of amplitude contrast
seismic attribute as disclosed herein. Accordingly, the scope of
amplitude contrast seismic attribute should be limited only by the
attached claims.